If your organization is planning to use AWS for deploying new releases, the deployment process can be tricky for teams outside of operations to learn and use, especially for those who don’t have expertise in the tooling to automate deployment. And, because there are several ways to deploy Kubernetes on AWS, including Amazon’s own EKS, understanding the different deployment options can be tough to navigate.
Today’s enterprise organizations are using some form of multi-cloud infrastructure, and the numbers don’t lie. According to Flexera’s 2020 State of the Cloud report, an average of 2.2 public clouds are being used per enterprise company. And in a different report from the Everest Group, 58% of enterprise workloads are on hybrid or private cloud. The sheer increase in multi-cloud usage illustrates it’s growing popularity across enterprises.
AI and Machine Learning (ML) are key priorities for enterprises, with a recent survey showing that 72% of CIOs expect to be heavy or moderate users of the technology. Unfortunately, other research has found that the vast majority—87%—of AI projects never make it into production. And even those that do often take 90 days or more to get there. Why this disconnect between intent and outcome? What are the roadblocks to enterprise ML? And what can be done about them?
With build-or-buy decisions, it often comes down to an all-in-one platform or a mixture of best-of-breed technologies. With open-source technology companies can actually get the best of everything. So, why not roll your own platform based on top-notch technologies? The real question is whether enterprises can afford to. Open-source software is free to use, but teams have to invest quite a bit in selecting, introducing, using, and maintaining these technologies.
AI is everywhere. Except in many enterprises. Going from a prototype to production is perilous when it comes to machine learning: most initiatives fail, and for the few models that are ever deployed, it takes many months to do so. While AI has the potential to transform and boost businesses, the reality for many companies is that machine learning only ever drips red ink on the balance sheet.
The latest versions of Konvoy and Kommander are now generally available: the D2iQ Kubernetes Platform (DKP) continues on its relentless roll. DKP is the leading independent Kubernetes platform for enterprise grade production at scale and Konvoy and Kommander are the reason why. You can learn more about Konvoy here, Kommander here, and our general approach here.
Today’s enterprises are pushing forward with their digital transformation initiatives to meet customer and market demand. The latest CNCF survey reports that 91% of companies are running Kubernetes and 81% of those companies are running Kubernetes in production. That’s up from 58% in 2018, and the numbers continue to ramp up quickly. There’s several approaches to how enterprises are thinking about adoption and their deployment and management of Kubernetes. I sat down with Lauren E.
In a relatively short amount of time, Kubernetes has evolved from an internal container orchestration tool at Google to the most important cloud-native technology across the world. Today’s enterprises are adopting Kubernetes to speed up the development process and build and deploy modern applications at scale, and they’re doing so at an incredible pace.
Thom McCann, senior manager and software engineer at T-Mobile, discussed how Kubernetes drove rapid growth and millions of dollars in savings, helping T-Mobile become the number two wireless provider in the U.S.
Kubernetes has become the default container orchestrator framework, setting the standard for application deployment in a distributed architecture. At D2iQ, we’re proud to work with some of the world’s largest organizations as they deploy and manage Kubernetes clusters on their cloud native journeys.
